Senior Machine Learning Engineer - ML Training Infrastructure

We are seeking an experienced, technical oriented, impact delivering-driven expert in ML Training Infrastructure with a strong ability to execute hands-on technical work. In this role, you will be responsible for designing and building scalable, reliable, and high-performance AI/ML platform infrastructure to support advanced AI research and model development initiatives. As a Senior ML Engineer, you will collaborate closely with machine learning engineers, research scientists, and other partners to develop state-of-the-art AI solutions that enable the future of intelligent driving technologies across General Motors vehicles.

• Design and development of scalable, reliable, high-performance ML framework to support model training at scale.
• Model training performance analysis and optimization solutions to scale distributed training workflows and maximize resource utilization across heterogeneous hardware environments, and save cost.
• Raise the bar on system observability, debuggability, and operational excellence, and user experience.
• Collaborate with cross-functional teams to integrate new features and technologies into the platform.

• Bachelors degree or higher in Computer Science or equivalent major OR equivalent relevant experience
• 2+ years professional software engineering experience.
• 2+ years specialized experience in AI/ML infrastructure, e.g., enabling distributed training for scaling large ML models
• Strong programming skills in Python, with proficiency in frameworks such as,PyTorch (preferred), TensorFlow, or similar
• Experience with distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure).
• Willingness to travel to Sunnyvale, CA as needed
• Comfortable working in highly ambiguous and dynamic environments

• 3+ years of professional software engineering experience.
• Self-motivated, strong execution, impact-delivering oriented
• Extensive knowledge and experience with PyTorch 2.x+ and distributed training framework
• Experience with design and development of training framework that supports FSDP, Pipeline Parallelism and other scalable solutions to training large foundational models
• Experience with profiling, analysis, debugging and optimizing training and data loading performance.
• Excellent communication skills to resolve controversial, make consensus, communicate risks and give constructive feedback

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area.

• The salary range for this role is $170,000 to $240,000. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.

Relocation: This job may be eligible for relocation benefits.

• Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.

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&#xa;&#xa;&#xa;&#xa;This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.&#xa;&#xa;The selected candidate will be required to travel <25% for this role.&#xa;&#xa;This job may be eligible for relocation benefits.&#xa;&#xa;

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